Overview

Dataset statistics

Number of variables15
Number of observations1327457
Missing cells263
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.9 MiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

issue_apm is highly correlated with structure_ratio and 1 other fieldsHigh correlation
structure_ratio is highly correlated with issue_apm and 1 other fieldsHigh correlation
land_ratio is highly correlated with t1_ratioHigh correlation
t1_ratio is highly correlated with issue_apm and 2 other fieldsHigh correlation
t2_ratio is highly correlated with t3_ratio and 1 other fieldsHigh correlation
t3_ratio is highly correlated with t2_ratioHigh correlation
defense_ratio is highly correlated with t2_ratioHigh correlation
issue_apm is highly correlated with structure_ratioHigh correlation
structure_ratio is highly correlated with issue_apm and 2 other fieldsHigh correlation
land_ratio is highly correlated with structure_ratio and 1 other fieldsHigh correlation
t1_ratio is highly correlated with structure_ratio and 1 other fieldsHigh correlation
issue_apm is highly correlated with structure_ratioHigh correlation
structure_ratio is highly correlated with issue_apm and 1 other fieldsHigh correlation
land_ratio is highly correlated with t1_ratioHigh correlation
t1_ratio is highly correlated with structure_ratio and 1 other fieldsHigh correlation
game_id is highly correlated with player_idHigh correlation
player_id is highly correlated with game_idHigh correlation
issue_apm is highly correlated with structure_ratio and 2 other fieldsHigh correlation
structure_ratio is highly correlated with issue_apm and 2 other fieldsHigh correlation
land_ratio is highly correlated with structure_ratio and 1 other fieldsHigh correlation
t1_ratio is highly correlated with issue_apm and 2 other fieldsHigh correlation
t2_ratio is highly correlated with issue_apmHigh correlation
t4_ratio is highly skewed (γ1 = 523.9540876) Skewed
reclaim_ratio has 184983 (13.9%) zeros Zeros
overcharge_ratio has 1142248 (86.0%) zeros Zeros
transport_ratio has 930667 (70.1%) zeros Zeros
air_ratio has 179076 (13.5%) zeros Zeros
naval_ratio has 1059757 (79.8%) zeros Zeros
t2_ratio has 234982 (17.7%) zeros Zeros
t3_ratio has 946491 (71.3%) zeros Zeros
t4_ratio has 1266415 (95.4%) zeros Zeros
defense_ratio has 227799 (17.2%) zeros Zeros

Reproduction

Analysis started2022-07-28 03:19:38.180085
Analysis finished2022-07-28 03:21:40.886703
Duration2 minutes and 2.71 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

game_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct680759
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10542194.94
Minimum4424784
Maximum17181303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:40.928959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4424784
5-th percentile4841314
Q17230264
median10527277
Q313746199
95-th percentile16411896
Maximum17181303
Range12756519
Interquartile range (IQR)6515935

Descriptive statistics

Standard deviation3733881.713
Coefficient of variation (CV)0.3541844688
Kurtosis-1.231493651
Mean10542194.94
Median Absolute Deviation (MAD)3261289
Skewness0.03161082887
Sum1.399431046 × 1013
Variance1.394187264 × 1013
MonotonicityNot monotonic
2022-07-27T23:21:40.995933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000062
 
< 0.1%
56891382
 
< 0.1%
56878662
 
< 0.1%
56878742
 
< 0.1%
56878922
 
< 0.1%
56879162
 
< 0.1%
56879412
 
< 0.1%
56879482
 
< 0.1%
56879642
 
< 0.1%
56880072
 
< 0.1%
Other values (680749)1327437
> 99.9%
ValueCountFrequency (%)
44247842
< 0.1%
44247862
< 0.1%
44248202
< 0.1%
44248342
< 0.1%
44248412
< 0.1%
44248722
< 0.1%
44248732
< 0.1%
44248962
< 0.1%
44248982
< 0.1%
44249222
< 0.1%
ValueCountFrequency (%)
171813032
< 0.1%
171812522
< 0.1%
171812212
< 0.1%
171812042
< 0.1%
171811752
< 0.1%
171811412
< 0.1%
171811402
< 0.1%
171810502
< 0.1%
171810172
< 0.1%
171809862
< 0.1%

player_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31058
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176389.4346
Minimum22
Maximum446864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.060296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile13043
Q169346
median162869
Q3271807
95-th percentile390331
Maximum446864
Range446842
Interquartile range (IQR)202461

Descriptive statistics

Standard deviation123047.1068
Coefficient of variation (CV)0.6975877387
Kurtosis-0.9837531045
Mean176389.4346
Median Absolute Deviation (MAD)97682
Skewness0.367418764
Sum2.341493897 × 1011
Variance1.51405905 × 1010
MonotonicityNot monotonic
2022-07-27T23:21:41.116940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11655818547
 
1.4%
144929303
 
0.7%
1452457920
 
0.6%
212347812
 
0.6%
159557688
 
0.6%
653277286
 
0.5%
4357083
 
0.5%
51476494
 
0.5%
1182135941
 
0.4%
254385917
 
0.4%
Other values (31048)1243466
93.7%
ValueCountFrequency (%)
22117
 
< 0.1%
2312
 
< 0.1%
24324
< 0.1%
361
 
< 0.1%
451
 
< 0.1%
4724
 
< 0.1%
5516
 
< 0.1%
6910
 
< 0.1%
7226
 
< 0.1%
9123
 
< 0.1%
ValueCountFrequency (%)
4468641
 
< 0.1%
4468358
< 0.1%
4468132
 
< 0.1%
4468021
 
< 0.1%
4467833
 
< 0.1%
4467521
 
< 0.1%
4467191
 
< 0.1%
44664816
< 0.1%
4464424
 
< 0.1%
4464101
 
< 0.1%

issue_apm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1080005
Distinct (%)81.4%
Missing263
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean34.49405492
Minimum6.565920523 × 10-6
Maximum251.6129032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.178345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.565920523 × 10-6
5-th percentile12.86278874
Q121.53798826
median30.79224755
Q344.37271653
95-th percentile67.65534068
Maximum251.6129032
Range251.6128967
Interquartile range (IQR)22.83472827

Descriptive statistics

Standard deviation17.32399606
Coefficient of variation (CV)0.5022313586
Kurtosis1.273288671
Mean34.49405492
Median Absolute Deviation (MAD)10.73737392
Skewness1.0266057
Sum45780302.72
Variance300.1208397
MonotonicityNot monotonic
2022-07-27T23:21:41.238997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301031
 
0.1%
12.24489796600
 
< 0.1%
12585
 
< 0.1%
10.90909091580
 
< 0.1%
11.53846154576
 
< 0.1%
11.32075472557
 
< 0.1%
13.63636364519
 
< 0.1%
11.76470588506
 
< 0.1%
12.5502
 
< 0.1%
11.11111111499
 
< 0.1%
Other values (1079995)1321239
99.5%
ValueCountFrequency (%)
6.565920523 × 10-61
< 0.1%
1.369064283 × 10-51
< 0.1%
0.031996587031
< 0.1%
0.03453237411
< 0.1%
0.048685491721
< 0.1%
0.054024851431
< 0.1%
0.068150840531
< 0.1%
0.080753701211
< 0.1%
0.082872928181
< 0.1%
0.083927822071
< 0.1%
ValueCountFrequency (%)
251.61290321
< 0.1%
226.81159421
< 0.1%
216.49484541
< 0.1%
212.3456791
< 0.1%
193.751
< 0.1%
192.85714291
< 0.1%
185.1063831
< 0.1%
183.87096771
< 0.1%
182.92682931
< 0.1%
180.54711251
< 0.1%

reclaim_ratio
Real number (ℝ≥0)

ZEROS

Distinct157981
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06184654418
Minimum0
Maximum1
Zeros184983
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.302025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.006741573034
median0.03121098627
Q30.08562197092
95-th percentile0.2269523057
Maximum1
Range1
Interquartile range (IQR)0.07888039789

Descriptive statistics

Standard deviation0.08192279968
Coefficient of variation (CV)1.324614023
Kurtosis8.988952493
Mean0.06184654418
Median Absolute Deviation (MAD)0.02911383972
Skewness2.491208745
Sum82098.628
Variance0.006711345108
MonotonicityNot monotonic
2022-07-27T23:21:41.362849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0184983
 
13.9%
0.07692307692851
 
0.1%
0.04166666667820
 
0.1%
0.03846153846817
 
0.1%
0.06666666667816
 
0.1%
0.07142857143807
 
0.1%
0.04347826087806
 
0.1%
0.04761904762804
 
0.1%
0.05555555556803
 
0.1%
0.05263157895803
 
0.1%
Other values (157971)1135147
85.5%
ValueCountFrequency (%)
0184983
13.9%
0.00022888532851
 
< 0.1%
0.00023391812871
 
< 0.1%
0.00024402147391
 
< 0.1%
0.0002468526291
 
< 0.1%
0.00025258903761
 
< 0.1%
0.00026007802341
 
< 0.1%
0.00026645350391
 
< 0.1%
0.00026716537541
 
< 0.1%
0.00027570995311
 
< 0.1%
ValueCountFrequency (%)
148
< 0.1%
0.93751
 
< 0.1%
0.89743589741
 
< 0.1%
0.89285714291
 
< 0.1%
0.8809523811
 
< 0.1%
0.87667560321
 
< 0.1%
0.8752
 
< 0.1%
0.8690476191
 
< 0.1%
0.86764705881
 
< 0.1%
0.86234817811
 
< 0.1%

overcharge_ratio
Real number (ℝ≥0)

ZEROS

Distinct21101
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001053043863
Minimum0
Maximum0.1922043011
Zeros1142248
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.424257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.007149240393
Maximum0.1922043011
Range0.1922043011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.004015170533
Coefficient of variation (CV)3.812918602
Kurtosis87.90849788
Mean0.001053043863
Median Absolute Deviation (MAD)0
Skewness7.188728344
Sum1397.870447
Variance1.612159441 × 10-5
MonotonicityNot monotonic
2022-07-27T23:21:41.618121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01142248
86.0%
0.005494505495163
 
< 0.1%
0.005524861878160
 
< 0.1%
0.006622516556158
 
< 0.1%
0.00625154
 
< 0.1%
0.007352941176153
 
< 0.1%
0.005102040816153
 
< 0.1%
0.0078125152
 
< 0.1%
0.007142857143151
 
< 0.1%
0.005917159763151
 
< 0.1%
Other values (21091)183814
 
13.8%
ValueCountFrequency (%)
01142248
86.0%
0.00013933398361
 
< 0.1%
0.00017205781141
 
< 0.1%
0.00018368846441
 
< 0.1%
0.0001925669171
 
< 0.1%
0.0002027575021
 
< 0.1%
0.00020673971471
 
< 0.1%
0.00021061499581
 
< 0.1%
0.00021299254531
 
< 0.1%
0.00021584286641
 
< 0.1%
ValueCountFrequency (%)
0.19220430111
< 0.1%
0.15921288011
< 0.1%
0.14448236631
< 0.1%
0.1386718751
< 0.1%
0.13821138211
< 0.1%
0.13636363641
< 0.1%
0.13227513231
< 0.1%
0.13226744191
< 0.1%
0.13084112151
< 0.1%
0.12910284461
< 0.1%

transport_ratio
Real number (ℝ≥0)

ZEROS

Distinct37137
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003734134572
Minimum0
Maximum0.3091603053
Zeros930667
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.681478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.004021447721
95-th percentile0.02019822461
Maximum0.3091603053
Range0.3091603053
Interquartile range (IQR)0.004021447721

Descriptive statistics

Standard deviation0.008136431788
Coefficient of variation (CV)2.178933735
Kurtosis25.86068011
Mean0.003734134572
Median Absolute Deviation (MAD)0
Skewness3.693555262
Sum4956.903076
Variance6.620152224 × 10-5
MonotonicityNot monotonic
2022-07-27T23:21:41.738181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0930667
70.1%
0.01449275362369
 
< 0.1%
0.01351351351369
 
< 0.1%
0.01408450704366
 
< 0.1%
0.01694915254366
 
< 0.1%
0.0119047619366
 
< 0.1%
0.009174311927364
 
< 0.1%
0.01538461538364
 
< 0.1%
0.01149425287361
 
< 0.1%
0.008695652174358
 
< 0.1%
Other values (37127)393507
29.6%
ValueCountFrequency (%)
0930667
70.1%
0.000194628261
 
< 0.1%
0.00023331777881
 
< 0.1%
0.00024975024981
 
< 0.1%
0.00025252525251
 
< 0.1%
0.00025303643721
 
< 0.1%
0.0002531645571
 
< 0.1%
0.0002561475411
 
< 0.1%
0.00025680534161
 
< 0.1%
0.00026434047051
 
< 0.1%
ValueCountFrequency (%)
0.30916030531
< 0.1%
0.30476190481
< 0.1%
0.22738095241
< 0.1%
0.21973094171
< 0.1%
0.21929824561
< 0.1%
0.20571428571
< 0.1%
0.20430107531
< 0.1%
0.2017543861
< 0.1%
0.19526627221
< 0.1%
0.19469026551
< 0.1%

structure_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct183210
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2215266485
Minimum0
Maximum1
Zeros2489
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.800104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07661290323
Q10.1279826464
median0.1812080537
Q30.2571428571
95-th percentile0.4845196041
Maximum1
Range1
Interquartile range (IQR)0.1291602107

Descriptive statistics

Standard deviation0.1640505984
Coefficient of variation (CV)0.7405456614
Kurtosis10.51549392
Mean0.2215266485
Median Absolute Deviation (MAD)0.06111276542
Skewness2.92910561
Sum294067.1002
Variance0.02691259884
MonotonicityNot monotonic
2022-07-27T23:21:41.861396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130961
 
2.3%
0.53679
 
0.3%
0.253240
 
0.2%
0.33333333333221
 
0.2%
0.23006
 
0.2%
0.16666666672593
 
0.2%
02489
 
0.2%
0.14285714291975
 
0.1%
0.66666666671823
 
0.1%
0.1251539
 
0.1%
Other values (183200)1272931
95.9%
ValueCountFrequency (%)
02489
0.2%
0.00090579710141
 
< 0.1%
0.0011560693641
 
< 0.1%
0.00156251
 
< 0.1%
0.00161
 
< 0.1%
0.001855287571
 
< 0.1%
0.0019417475731
 
< 0.1%
0.002053388091
 
< 0.1%
0.0020876826721
 
< 0.1%
0.0021834061141
 
< 0.1%
ValueCountFrequency (%)
130961
2.3%
0.99361022361
 
< 0.1%
0.9870129871
 
< 0.1%
0.98648648651
 
< 0.1%
0.98275862072
 
< 0.1%
0.98113207551
 
< 0.1%
0.98039215691
 
< 0.1%
0.97727272731
 
< 0.1%
0.97560975611
 
< 0.1%
0.97368421051
 
< 0.1%

air_ratio
Real number (ℝ≥0)

ZEROS

Distinct89535
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03259337437
Minimum0
Maximum1
Zeros179076
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:41.924748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01123595506
median0.02450980392
Q30.0431372549
95-th percentile0.08722741433
Maximum1
Range1
Interquartile range (IQR)0.03190129985

Descriptive statistics

Standard deviation0.04459548684
Coefficient of variation (CV)1.3682378
Kurtosis222.3843749
Mean0.03259337437
Median Absolute Deviation (MAD)0.01527799449
Skewness11.40241227
Sum43266.30297
Variance0.001988757447
MonotonicityNot monotonic
2022-07-27T23:21:41.984557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0179076
 
13.5%
0.035714285711565
 
0.1%
0.032258064521496
 
0.1%
0.034482758621481
 
0.1%
0.037037037041477
 
0.1%
0.033333333331474
 
0.1%
0.038461538461467
 
0.1%
0.03030303031461
 
0.1%
0.028571428571460
 
0.1%
0.045454545451455
 
0.1%
Other values (89525)1135045
85.5%
ValueCountFrequency (%)
0179076
13.5%
0.00028481913981
 
< 0.1%
0.00031172069831
 
< 0.1%
0.00031338138511
 
< 0.1%
0.00032351989651
 
< 0.1%
0.00034048348661
 
< 0.1%
0.0003409478351
 
< 0.1%
0.00034270047981
 
< 0.1%
0.00036036036041
 
< 0.1%
0.00036536353671
 
< 0.1%
ValueCountFrequency (%)
11273
0.1%
0.95901639341
 
< 0.1%
0.88888888892
 
< 0.1%
0.8752
 
< 0.1%
0.85714285712
 
< 0.1%
0.83333333331
 
< 0.1%
0.82758620691
 
< 0.1%
0.81
 
< 0.1%
0.79166666671
 
< 0.1%
0.77272727271
 
< 0.1%

land_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct157701
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1473298496
Minimum0
Maximum1
Zeros7531
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.044988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03773584906
Q10.07698961938
median0.1151515152
Q30.1712062257
95-th percentile0.3333333333
Maximum1
Range1
Interquartile range (IQR)0.0942166063

Descriptive statistics

Standard deviation0.1354591488
Coefficient of variation (CV)0.9194277275
Kurtosis20.12508459
Mean0.1473298496
Median Absolute Deviation (MAD)0.04423460971
Skewness3.9215678
Sum195574.0402
Variance0.01834918101
MonotonicityNot monotonic
2022-07-27T23:21:42.105353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117323
 
1.3%
07531
 
0.6%
0.33333333335008
 
0.4%
0.54927
 
0.4%
0.24355
 
0.3%
0.254341
 
0.3%
0.16666666674098
 
0.3%
0.14285714293771
 
0.3%
0.1253071
 
0.2%
0.11111111112702
 
0.2%
Other values (157691)1270330
95.7%
ValueCountFrequency (%)
07531
0.6%
0.0010940919041
 
< 0.1%
0.0010989010991
 
< 0.1%
0.0011312217191
 
< 0.1%
0.0012224938881
 
< 0.1%
0.0012554927811
 
< 0.1%
0.0013386880861
 
< 0.1%
0.0013422818791
 
< 0.1%
0.0014275517491
 
< 0.1%
0.0014534883721
 
< 0.1%
ValueCountFrequency (%)
117323
1.3%
0.97727272731
 
< 0.1%
0.9767441861
 
< 0.1%
0.97435897441
 
< 0.1%
0.968753
 
< 0.1%
0.96774193551
 
< 0.1%
0.96666666671
 
< 0.1%
0.96551724141
 
< 0.1%
0.9629629632
 
< 0.1%
0.96153846151
 
< 0.1%

naval_ratio
Real number (ℝ≥0)

ZEROS

Distinct51401
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006293813221
Minimum0
Maximum1
Zeros1059757
Zeros (%)79.8%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.168562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.04176904177
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.01691427038
Coefficient of variation (CV)2.687443968
Kurtosis67.91057647
Mean0.006293813221
Median Absolute Deviation (MAD)0
Skewness4.670360223
Sum8354.766418
Variance0.0002860925424
MonotonicityNot monotonic
2022-07-27T23:21:42.228362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01059757
79.8%
0.03125304
 
< 0.1%
0.04166666667291
 
< 0.1%
0.0303030303291
 
< 0.1%
0.03703703704289
 
< 0.1%
0.03225806452276
 
< 0.1%
0.02564102564276
 
< 0.1%
0.0243902439275
 
< 0.1%
0.04274
 
< 0.1%
0.04545454545274
 
< 0.1%
Other values (51391)265150
 
20.0%
ValueCountFrequency (%)
01059757
79.8%
0.00025706940871
 
< 0.1%
0.00026301946341
 
< 0.1%
0.00027041644131
 
< 0.1%
0.00029682398341
 
< 0.1%
0.00030703101011
 
< 0.1%
0.00030959752321
 
< 0.1%
0.0003132832081
 
< 0.1%
0.00031357792411
 
< 0.1%
0.0003204101251
 
< 0.1%
ValueCountFrequency (%)
14
< 0.1%
0.951
 
< 0.1%
0.54
< 0.1%
0.4517543861
 
< 0.1%
0.44444444441
 
< 0.1%
0.44055944061
 
< 0.1%
0.38281251
 
< 0.1%
0.3751
 
< 0.1%
0.36363636361
 
< 0.1%
0.35714285711
 
< 0.1%

t1_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct188568
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2976308655
Minimum0
Maximum1
Zeros2481
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.290031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1112099644
Q10.1822503962
median0.2524752475
Q30.3520179372
95-th percentile0.6666666667
Maximum1
Range1
Interquartile range (IQR)0.169767541

Descriptive statistics

Standard deviation0.182605301
Coefficient of variation (CV)0.6135294493
Kurtosis4.834715322
Mean0.2976308655
Median Absolute Deviation (MAD)0.08078151203
Skewness2.023291706
Sum395092.1758
Variance0.03334469595
MonotonicityNot monotonic
2022-07-27T23:21:42.349340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135068
 
2.6%
0.53206
 
0.2%
0.33333333333064
 
0.2%
0.252521
 
0.2%
02481
 
0.2%
0.66666666672185
 
0.2%
0.751894
 
0.1%
0.21759
 
0.1%
0.81583
 
0.1%
0.83333333331508
 
0.1%
Other values (188558)1272188
95.8%
ValueCountFrequency (%)
02481
0.2%
0.0018115942031
 
< 0.1%
0.00221729491
 
< 0.1%
0.0025062656641
 
< 0.1%
0.0026178010471
 
< 0.1%
0.0026809651471
 
< 0.1%
0.0032051282051
 
< 0.1%
0.0041753653441
 
< 0.1%
0.0058823529411
 
< 0.1%
0.006251
 
< 0.1%
ValueCountFrequency (%)
135068
2.6%
0.9870129872
 
< 0.1%
0.98684210531
 
< 0.1%
0.98611111111
 
< 0.1%
0.98550724641
 
< 0.1%
0.98529411763
 
< 0.1%
0.98484848482
 
< 0.1%
0.98461538462
 
< 0.1%
0.98412698412
 
< 0.1%
0.98387096771
 
< 0.1%

t2_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct131833
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04321170463
Minimum0
Maximum0.9776357827
Zeros234982
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.410276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.004405286344
median0.02578796562
Q30.06265356265
95-th percentile0.1460674157
Maximum0.9776357827
Range0.9776357827
Interquartile range (IQR)0.05824827631

Descriptive statistics

Standard deviation0.05357911382
Coefficient of variation (CV)1.23992132
Kurtosis10.95193654
Mean0.04321170463
Median Absolute Deviation (MAD)0.02449430456
Skewness2.504736442
Sum57361.67979
Variance0.002870721438
MonotonicityNot monotonic
2022-07-27T23:21:42.471139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0234982
 
17.7%
0.05882352941840
 
0.1%
0.05263157895822
 
0.1%
0.07692307692810
 
0.1%
0.0625810
 
0.1%
0.05555555556801
 
0.1%
0.03846153846799
 
0.1%
0.05796
 
0.1%
0.04166666667783
 
0.1%
0.06666666667781
 
0.1%
Other values (131823)1085233
81.8%
ValueCountFrequency (%)
0234982
17.7%
0.00033101621981
 
< 0.1%
0.00033726812821
 
< 0.1%
0.00035714285711
 
< 0.1%
0.00037439161361
 
< 0.1%
0.00037650602411
 
< 0.1%
0.00037807183361
 
< 0.1%
0.00038387715931
 
< 0.1%
0.00042735042741
 
< 0.1%
0.00043668122271
 
< 0.1%
ValueCountFrequency (%)
0.97763578271
 
< 0.1%
0.90697674421
 
< 0.1%
0.88888888891
 
< 0.1%
0.8753
< 0.1%
0.85714285712
< 0.1%
0.81251
 
< 0.1%
0.81111111111
 
< 0.1%
0.80512820511
 
< 0.1%
0.81
 
< 0.1%
0.77777777782
< 0.1%

t3_ratio
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct76783
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007015405283
Minimum0
Maximum1
Zeros946491
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.532831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.002044989775
95-th percentile0.04371584699
Maximum1
Range1
Interquartile range (IQR)0.002044989775

Descriptive statistics

Standard deviation0.02061846572
Coefficient of variation (CV)2.939027025
Kurtosis60.53709401
Mean0.007015405283
Median Absolute Deviation (MAD)0
Skewness5.485198705
Sum9312.648851
Variance0.0004251211286
MonotonicityNot monotonic
2022-07-27T23:21:42.592739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0946491
71.3%
0.003984063745189
 
< 0.1%
0.003115264798183
 
< 0.1%
0.003424657534183
 
< 0.1%
0.004016064257183
 
< 0.1%
0.003355704698182
 
< 0.1%
0.003448275862181
 
< 0.1%
0.003215434084180
 
< 0.1%
0.003134796238179
 
< 0.1%
0.005050505051178
 
< 0.1%
Other values (76773)379328
28.6%
ValueCountFrequency (%)
0946491
71.3%
0.00021584286641
 
< 0.1%
0.00022815423231
 
< 0.1%
0.00025766555011
 
< 0.1%
0.00026497085321
 
< 0.1%
0.00027137042061
 
< 0.1%
0.00027495188341
 
< 0.1%
0.00028042624791
 
< 0.1%
0.00028951939781
 
< 0.1%
0.00029429075931
 
< 0.1%
ValueCountFrequency (%)
16
< 0.1%
0.66666666671
 
< 0.1%
0.64285714291
 
< 0.1%
0.56737588651
 
< 0.1%
0.53
< 0.1%
0.47539742621
 
< 0.1%
0.45602165091
 
< 0.1%
0.44602551521
 
< 0.1%
0.43614457831
 
< 0.1%
0.41821561341
 
< 0.1%

t4_ratio
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct10441
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001065681643
Minimum0
Maximum1
Zeros1266415
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.653089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.001132214919
Coefficient of variation (CV)10.624326
Kurtosis458372.5156
Mean0.0001065681643
Median Absolute Deviation (MAD)0
Skewness523.9540876
Sum141.4646557
Variance1.281910623 × 10-6
MonotonicityNot monotonic
2022-07-27T23:21:42.712639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01266415
95.4%
0.00197628458561
 
< 0.1%
0.00191204588960
 
< 0.1%
0.00180505415259
 
< 0.1%
0.00179533213659
 
< 0.1%
0.00189753320758
 
< 0.1%
0.00155038759756
 
< 0.1%
0.00161030595855
 
< 0.1%
0.00157977883155
 
< 0.1%
0.00154320987754
 
< 0.1%
Other values (10431)60525
 
4.6%
ValueCountFrequency (%)
01266415
95.4%
0.00012759984691
 
< 0.1%
0.00015946420031
 
< 0.1%
0.00016874789071
 
< 0.1%
0.00017358097551
 
< 0.1%
0.00017503938391
 
< 0.1%
0.00017680339461
 
< 0.1%
0.00017831669041
 
< 0.1%
0.00018050541521
 
< 0.1%
0.00018155410311
 
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.080516287651
< 0.1%
0.06903163951
< 0.1%
0.064663023681
< 0.1%
0.063139931741
< 0.1%
0.06265664161
< 0.1%
0.062407132241
< 0.1%
0.060319337671
< 0.1%
0.060118543611
< 0.1%
0.059347181011
< 0.1%

defense_ratio
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct88884
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02267242342
Minimum0
Maximum1
Zeros227799
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size10.1 MiB
2022-07-27T23:21:42.773307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003636363636
median0.0126459144
Q30.02941176471
95-th percentile0.07987220447
Maximum1
Range1
Interquartile range (IQR)0.02577540107

Descriptive statistics

Standard deviation0.03261947666
Coefficient of variation (CV)1.438729158
Kurtosis115.0229925
Mean0.02267242342
Median Absolute Deviation (MAD)0.01100657013
Skewness6.282074313
Sum30096.66718
Variance0.001064030257
MonotonicityNot monotonic
2022-07-27T23:21:42.833933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227799
 
17.2%
0.02173913043941
 
0.1%
0.02325581395930
 
0.1%
0.025930
 
0.1%
0.02127659574927
 
0.1%
0.02083333333922
 
0.1%
0.01639344262919
 
0.1%
0.01818181818917
 
0.1%
0.02564102564916
 
0.1%
0.0303030303915
 
0.1%
Other values (88874)1091341
82.2%
ValueCountFrequency (%)
0227799
17.2%
0.00026867275661
 
< 0.1%
0.0002797985451
 
< 0.1%
0.00028105677351
 
< 0.1%
0.00030084235861
 
< 0.1%
0.00030883261271
 
< 0.1%
0.00032615786041
 
< 0.1%
0.00035714285711
 
< 0.1%
0.00036324010171
 
< 0.1%
0.0003665689151
 
< 0.1%
ValueCountFrequency (%)
1108
< 0.1%
0.9870129871
 
< 0.1%
0.98275862071
 
< 0.1%
0.98039215691
 
< 0.1%
0.97727272731
 
< 0.1%
0.97368421051
 
< 0.1%
0.9729729731
 
< 0.1%
0.97142857141
 
< 0.1%
0.97014925371
 
< 0.1%
0.96774193551
 
< 0.1%

Interactions

2022-07-27T23:21:33.585482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:44.655953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:48.153578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:51.793207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:55.543799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:59.161212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:02.463370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:05.936708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:09.521790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:12.995308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:16.686701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:19.955565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:23.556082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:27.130216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:30.353442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:33.813986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:44.879664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:48.395955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:52.054529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:55.909420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:59.388445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:02.691022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:06.181464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:09.754535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:13.236735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:16.910562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:20.196811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:23.916678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:27.350904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:30.567600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:34.051669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:45.117946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:48.650816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:52.299699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:56.156432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:59.619491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:02.926521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:06.431643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:09.994339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:13.484397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:17.137914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:20.445015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:24.159017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:27.581296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:30.785135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:34.278944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:45.341589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:48.893088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:52.552798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:06.671061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:10.227444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:20.684192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:24.390330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:30.992062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:20:49.131660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:52.800565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:56.615939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:00.054110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:03.365734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:06.907220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:10.454712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:13.955515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:17.565276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:20.922365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:24.617866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:28.011403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:31.194485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:34.717000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:45.967113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:49.375179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:53.052886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:56.852715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:00.275229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:03.585544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:07.151236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:10.683810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:14.327464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:17.783310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:24.850872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:28.225663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:31.399362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:34.948390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:46.187886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:20:53.308092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:57.091676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:00.501673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:03.811274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:07.391782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:10.922200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:25.086722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:28.446640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:20:53.814199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:01.381995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:15.768323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:26.241530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:29.520610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:32.766610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:36.289234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:20:51.065791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:54.814519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:12.292097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:16.004467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:19.292304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:26.467014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:29.729083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:36.507416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:47.698292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:05.477923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:09.057561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:12.516960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:21:29.936844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:33.162089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:36.724553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:47.913148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:20:51.541114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-07-27T23:20:58.953095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:02.243318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:05.697204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:09.295811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:12.753083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:16.477004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:19.716854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:23.329402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:26.921095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:30.149274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T23:21:33.365881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-27T23:21:42.892361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-27T23:21:43.115939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-27T23:21:43.213950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-27T23:21:43.311650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-27T23:21:36.839311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-27T23:21:38.217871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-27T23:21:40.278285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

game_idplayer_idissue_apmreclaim_ratioovercharge_ratiotransport_ratiostructure_ratioair_ratioland_rationaval_ratiot1_ratiot2_ratiot3_ratiot4_ratiodefense_ratio
01000000630420533.3009430.0226570.00.0000000.1410920.0175080.0782700.0000000.1225540.0659110.0195670.0010300.008239
11000000625819846.1194030.0298730.00.0044810.1090370.0119490.0470500.0000000.1030620.0485440.0067210.0000000.052278
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Last rows

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